{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SEIRS+ Network Model Demo\n",
    "\n",
    "**This notebook provides a demonstration of the core functionality of the SEIRS+ Network Model and offers a sandbox for easily changing simulation parameters and scenarios.** \n",
    "For a more thorough walkthrough of the model and use of this package, refer to the README."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing and Importing the model code\n",
    "All of the code needed to run the model is imported from the ```models``` module of this package.\n",
    "\n",
    "#### Install the package using ```pip```\n",
    "The package can be installed on your machine by entering this in the command line:\n",
    "\n",
    "```sudo pip install seirsplus```\n",
    "\n",
    "Then, the ```models``` module can be imported into your scripts as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seirsplus.models import *\n",
    "import networkx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Alternatively, manually copy the code to your machine*\n",
    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. In this case, the easiest way to use the module is to place your scripts in the same directory as the module, and import the module as shown here:*\n",
    "```python\n",
    "from models import *\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating interaction networks\n",
    "\n",
    "This model simulates SEIRS epidemic dynamics for populations with a structured interaction network (as opposed to standard deterministic SIR/SEIR/SEIRS models, which assume uniform mixing of the population). As such, a graph specifying the interaction network for the population must be specified, where each node represents an individual in the population and edges connect individuals who have regular interactions.\n",
    "\n",
    "The interaction network can be specified by a ```networkx``` Graph object or a 2D numpy array representing the adjacency matrix, either of which can be defined and generated by any method.\n",
    "\n",
    "*Here, we use a ```custom_exponential_graph()``` generation function included in this package, which generates power-law graphs that have degree distributions with two exponential tails. For more information on this custom graph type and its generation, see the README.*\n",
    "\n",
    "**_Note:_** *Simulation time increases with network size. Small networks simulate quickly, but have more stochastic volatility. Networks with ~10,000 are large enough to produce per-capita population dynamics that are generally consistent with those of larger networks, but small enough to simulate quickly. We recommend using networks with ~10,000 nodes for prototyping parameters and scenarios, which can then be run on larger networks if more precision is required (for more on this, see README).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Kx6la27Bhg7lgwQLTNE2zrq7OHDt2rN/tz7Yy+uO+fPPNN82cnBzTNE3zb3/7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b41ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numNodes = 10000\n",
    "baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=9)\n",
    "# Baseline normal interactions:\n",
    "G_normal     = custom_exponential_graph(baseGraph, scale=100)\n",
    "plot_degree_distn(G_normal, max_degree=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic scenarios of interest often involve interaction networks that change in time. Multiple interaction networks can be defined and used at different times in the model simulation, as will be shown below.\n",
    "\n",
    "*Here we generate a graph representing interactions during corresponding to Social Distancing, where each individual drops some portion of their normal interactions with others. Again, we use the ```custom_exponential_graph()``` to generate this graph; for more information, see the README.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iItIyGgyN7Ozs07YVFxezYsUKxowZ0+gTBQUF8dJLL522PTc397RtKSkppKSkNPocIiLS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\n0xQaIiJimkJDRERM+3/MptK2VsO9ugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d8f9510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Social distancing interactions:\n",
    "G_distancing = custom_exponential_graph(baseGraph, scale=10)\n",
    "plot_degree_distn(G_distancing, max_degree=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This SEIRS+ model features dynamics corresponding to testing individuals for the disease and moving individuals with detected infection into a state where their rate of recovery, mortality, etc may be different. In addition, given that this model considers individuals in an interaction network, a separate graph defining the interactions for individuals with detected cases can be specified.\n",
    "\n",
    "*Here we generate a graph representing the interactions that individuals have when they test positive for the disease. In this case, a significant portion of each individual's normal interaction edges are removed from the graph, as if the individual is quarantined upon detection of infection. Again, we use the ```custom_exponential_graph()``` to generate this graph; for more information, see the README.*\n",
    "\n",
    "For more information on how testing, contact tracing, and detected cases are handled in this model, see the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YOnToFT/J0KFD2bp1KwBff/017du3JzIykjVr1mC32zl48CB2u53AwEDatWvH+vXrASgq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\nERFTVBgiImKKCkNERExRYYiIiCn/B7Yp3e8Um318AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e8aadd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quarantine interactions:\n",
    "G_quarantine = custom_exponential_graph(baseGraph, scale=5)\n",
    "plot_degree_distn(G_quarantine, max_degree=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing the model parameters\n",
    "All model parameter values, including the normal and quarantine interaction networks, are set in the call to the ```SEIRSNetworkModel``` constructor. The normal interaction network ```G``` and the basic SEIR parameters ```beta```, ```sigma```, and ```gamma``` are the only required arguments. All other arguments represent parameters for optional extended model dynamics; these optional parameters take default values that turn off their corresponding dynamics when not provided in the constructor. For clarity and ease of customization in this notebook, all available model parameters are listed below. \n",
    "\n",
    "For more information on parameter meanings, see the README.\n",
    "\n",
    "*The parameter values shown correspond to rough estimates of parameter values for the COVID-19 epidemic.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SEIRSNetworkModel(G       =G_normal, \n",
    "                          beta    =0.155, \n",
    "                          sigma   =1/5.2, \n",
    "                          gamma   =1/12.39, \n",
    "                          mu_I    =0.0004,\n",
    "                          mu_0    =0, \n",
    "                          nu      =0, \n",
    "                          xi      =0,\n",
    "                          p       =0.5,\n",
    "                          Q       =G_quarantine, \n",
    "                          beta_D  =0.155, \n",
    "                          sigma_D =1/5.2, \n",
    "                          gamma_D =1/12.39, \n",
    "                          mu_D    =0.0004,\n",
    "                          theta_E =0, \n",
    "                          theta_I =0, \n",
    "                          phi_E   =0, \n",
    "                          phi_I   =0, \n",
    "                          psi_E   =1.0, \n",
    "                          psi_I   =1.0,\n",
    "                          q       =0.5,\n",
    "                          initI   =numNodes/100, \n",
    "                          initE   =0, \n",
    "                          initD_E =0, \n",
    "                          initD_I =0, \n",
    "                          initR   =0, \n",
    "                          initF   =0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkpoints\n",
    "Model parameters can be easily changed during a simulation run using checkpoints. A dictionary holds a list of checkpoint times (```checkpoints['t']```) and lists of new values to assign to various model parameters at each checkpoint time. Any model parameter listed in the model constrcutor can be updated in this way. Only model parameters that are included in the checkpoints dictionary have their values updated at the checkpoint times, all other parameters keep their pre-existing values.\n",
    "\n",
    "*The checkpoints shown here correspond to starting social distancing and testing at time ```t=20``` (the graph ```G``` is updated to ```G_distancing``` and locality parameter ```p``` is decreased to ```0.1```; testing params ```theta_E```, ```theta_I```, ```phi```, and ```phi_I``` are set to non-zero values) and then stopping social distancing at time ```t=100``` (```G``` and ```p``` changed back to their \"normal\" values; testing params remain non-zero).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoints = {'t':       [20, 100], \n",
    "               'G':       [G_distancing, G_normal], \n",
    "               'p':       [0.1, 0.5], \n",
    "               'theta_E': [0.02, 0.02], \n",
    "               'theta_I': [0.02, 0.02], \n",
    "               'phi_E':   [0.2, 0.2], \n",
    "               'phi_I':   [0.2, 0.2]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 0.02\n",
      "t = 10.03\n",
      "[Checkpoint: Updating parameters]\n",
      "t = 20.07\n",
      "t = 30.02\n",
      "t = 40.01\n",
      "t = 50.02\n",
      "t = 60.01\n",
      "t = 70.03\n",
      "t = 80.03\n",
      "t = 90.02\n",
      "[Checkpoint: Updating parameters]\n",
      "t = 100.03\n",
      "t = 110.01\n",
      "t = 120.05\n",
      "t = 130.00\n",
      "t = 140.07\n",
      "t = 150.01\n",
      "t = 160.00\n",
      "t = 170.04\n",
      "t = 180.01\n",
      "t = 190.00\n",
      "t = 200.02\n",
      "t = 210.01\n",
      "t = 220.00\n",
      "t = 230.00\n",
      "t = 240.02\n",
      "t = 250.01\n",
      "t = 260.04\n",
      "t = 270.03\n",
      "t = 280.08\n",
      "t = 290.01\n",
      "t = 300.18\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.run(T=300, checkpoints=checkpoints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the results\n",
    "The ```SEIRSNetworkModel``` class has a ```plot()``` convenience function for plotting simulation results on a matplotlib axis. This function generates a line plot of the frequency of each model state in the population by default, but there are many optional arguments that can be used to customize the plot.\n",
    "\n",
    "The ```SEIRSNetworkModel``` class also has convenience functions for generating a full figure out of model simulation results (optionaly arguments can be provided to customize the plots generated by these functions). \n",
    "- ```figure_basic()``` calls the ```plot()``` function with default parameters to generate a line plot of the frequency of each state in the population.\n",
    "- ```figure_infections()``` calls the ```plot()``` function with default parameters to generate a stacked area plot of the frequency of only the infection states ($E$, $I$, $D_E$, $D_I$) in the population.\n",
    "\n",
    "For more information on the built-in plotting functions, see the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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kqXPnzjYnAaqOOgYAwBhHIBAI2B2iqtLS0pSRkWF3jPPm8/kkSRERETYnAaqOOgYAwBhW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\nbkDJyWyc64EQoWdfB0nCvLgWYnHk3BwCv/lrSj/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zY1EHiJkKM97ruGfmOCKuvik1F/+h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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d8eab90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reference simulation visualizations\n",
    "\n",
    "We can also visualize the results of other simulation(s) as a reference for comparison of our main simulation.\n",
    "\n",
    "Here we simulate a model where no distancing or testing takes place, so that we can compare the effects of these interventions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 0.04\n",
      "t = 10.07\n",
      "t = 20.02\n",
      "t = 30.01\n",
      "t = 40.02\n",
      "t = 50.00\n",
      "t = 60.00\n",
      "t = 70.00\n",
      "t = 80.00\n",
      "t = 90.00\n",
      "t = 100.00\n",
      "t = 110.00\n",
      "t = 120.02\n",
      "t = 130.01\n",
      "t = 140.00\n",
      "t = 150.00\n",
      "t = 160.03\n",
      "t = 170.01\n",
      "t = 180.02\n",
      "t = 190.05\n",
      "t = 200.10\n",
      "t = 210.21\n",
      "t = 220.16\n",
      "t = 230.41\n",
      "t = 240.11\n",
      "t = 250.09\n",
      "t = 260.03\n",
      "t = 270.11\n",
      "t = 280.61\n",
      "t = 290.45\n",
      "t = 300.28\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_model = SEIRSNetworkModel(G=G_normal, beta=0.155, sigma=1/5.2, gamma=1/12.39, mu_I=0.0004, p=0.5,\n",
    "                          Q=G_quarantine, beta_D=0.155, sigma_D=1/5.2, gamma_D=1/12.39, mu_D=0.0004,\n",
    "                          theta_E=0, theta_I=0, phi_E=0, phi_I=0, psi_E=1.0, psi_I=1.0, q=0.5,\n",
    "                          initI=numNodes/100)\n",
    "ref_model.run(T=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can visualize our main simulation together with this reference simulation by passing the model object of the reference simulation to the appropriate figure function argument (note: a second reference simulation could also be visualized by passing it to the ```dashed_reference_results``` argument):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eVpKusixLlmXpxBNP7HBfaj8y3uv1hv49lJWV6YQTTlBOTo5s21YgEOjVv2wA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\nAAexw+2gXbt2hfpfk0V5ebmkjoeAIL5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K2/+4ZXRbjJpHHnkEgBtuuGGKZ2Ix2USj0RTp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2TaBbjDjULh2A62Y4NyaNluExjp4G35Az73Pg6WrmTC5S4M0f79SIQcLK/R9QKp7ZjRw8S2v0JZ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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12483f450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.2, shaded_reference_results=ref_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As further demonstration, we might also wish to compare the results of these network model simulations to a deterministic model simulation of the same SEIRS parameters (with no interventions in this case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 299.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_model_determ = SEIRSModel(beta=0.147, sigma=1/5.2, gamma=1/12.39, mu_I=0.0004, initI=100, initN=10000) \n",
    "ref_model_determ.run(T=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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raOrUqWpHIIobxzEREZE8bCkhIiIiIkogFtwK+uijj/DRRx+pHYMoLhzHRERE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\nNBqNRqPRTCBacGs0Go1Go9FoNBPI/weoYoHLV3MGXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12483fb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.2, \n",
    "                        shaded_reference_results=ref_model, shaded_reference_label='network: no interventions',\n",
    "                        dashed_reference_results=ref_model_determ, dashed_reference_label='deterministic: no interventions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
